Artificial neural networks generally consist of multiple layers of neuron devices which provide massively parallel computing power. An intriguing feature of such networks is their adaptive capabilities which allow the network to learn new information. These characteristics provide parallel processing of information at high computational rates--far exceeding the performance of conventional Von Neumann computers which execute a program of instructions sequentially.
Neural networks generally take the form of a matrix of connections which simulate the function of a biological nervous system. Typically, electrical circuits are employed to provide variable strength synaptic connections between a plurality of inputs and a number of summing elements (i.e., neurons). The strength of the interconnections is commonly referred as the "weight" of the network. The synaptic weight, which frequently changes during the training or learning process, basically modulates the amount of charge or voltage input into each neuron within the network.
In the past, electrical synapse cells which employ floating gate devices have been used for storing connection weights in the form of electrical charge on the floating gate. In a floating gate device, current flow is modulated in a way which depends on the value of the stored electrical charge. In these cells, a dot product calculation is normally performed wherein the applied input voltage is multiplied by the stored weight to produce an output. This output is then summed with other outputs in the network. Examples of semiconductor synapse cells which employ floating gate devices for storing weights are found in U.S. Pat. Nos. 4,956,564 and 4,961,002.
Another category of neural network computes the difference between the input value and stored weight value. This type of network performs what is frequently referred to as a "Manhattan Distance" or "City Block Distance" calculation. Both the multiplication and difference types of neural networks are equally capable of solving computational tasks such as associative memory and pattern classification.
As will be seen, the present invention discloses a neural network which calculates the absolute "City Block Distance" between an input voltage and a stored weight. The invented network utilizes switched-capacitor circuitry and floating gate devices for emulating the function of a biological synapse.
Other prior art known to applicant includes an article by K. Suyama et al., entitled, "Simulation of Mixed Switched-Capacitor/Digital Networks with Signal-Driven Switches," IEEE Journal of Solid-State Circuits, Vol. 25, No. 6, December 1990.